Position of the South Atlantic Anticyclone and Its ... - AMS Journals

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Position of the South Atlantic Anticyclone and Its Impact on Surface Conditions across Brazil JOSHUA M. GILLILAND AND BARRY D. KEIM Department of Geography and Anthropology, Louisiana State University, Baton Rouge, Louisiana (Manuscript received 23 June 2017, in final form 2 November 2017) ABSTRACT This study examines the surface wind characteristics of Brazil on the basis of the location of the maximum high pressure center in the South Atlantic basin (SAB), known as the South Atlantic anticyclone (SAA), from three reanalysis datasets for the period of 1980–2014. Linear wind speed trends determined for Brazil are geographically related to surface and macroscale atmospheric conditions found in the SAB. The daily mean position of the SAA exhibited a latitudinal poleward shift for all seasons, and a longitudinal trend was dependent upon extratropical activity found in the SAB. Results also show that wind speed and sea level pressure for northern Brazil are dependent upon the latitudinal position of the SAA. Consequently, surface wind correlations for southern Brazil tend to be related to changes in the longitudinal position of the SAA, which result from transient anticyclones migrating over the SAB. An examination of positive and negative wind anomalies shows that shifts in the position of the SAA are coupled with changes in sea level pressure for northern Brazil and air temperature for southern Brazil. From these findings, a surface wind analysis was performed to demonstrate how the geographical location of the SAA affects wind speed anomalies across Brazil and the SAB. Results from this study can assist in understanding how atmospheric systems change within the SAB so that forthcoming socioeconomic and climate-related causes of wind for the country of Brazil can be known.

1. Introduction The climatological position of anticyclones has been identified and examined to understand how their spatial and temporal characteristics have changed across the Northern Hemisphere (Klein 1958; Zishka and Smith 1980; Harman 1987; Bell and Bosart 1989; Parker et al. 1989; Sahsamanoglou 1990; Alberta et al. 1991; Davis et al. 1997; Kapala et al. 1998; Harvey et al. 2002; Favre and Gershunov 2006; Galarneau et al. 2008; Ioannidou and Yau 2008; Zarrin et al. 2010; Chen et al. 2014; Hatzaki et al. 2014; Voskresenskaya et al. 2016) and Southern Hemisphere (Taljaard 1967; Jones and Simmonds 1994; Leighton 1994; Leighton and Nowak 1995; Sinclair 1996, 1997; Mächel et al. 1998; Pezza and Ambrizzi 2003; Galarneau et al. 2008; Pezza et al. 2007) over the last century. Recently, surface wind studies have suggested that modifications in the position of macroscale atmospheric systems could explain the spatial and temporal wind trends found across the Northern Hemisphere (Tuller 2004; St. George and Wolfe 2009; Corresponding author: Joshua M. Gilliland, [email protected]

Abhishek et al. 2010; Jiang et al. 2010; Li et al. 2010; Pryor and Ledolter 2010; You et al. 2010; Fu et al. 2011; Hewston and Dorling 2011; Yang et al. 2012; Chen et al. 2013; Lin et al. 2013; You et al. 2014; Nchaba et al. 2017). This reason is consistent with wind speed trends found for coastal and northeastern Brazil, which are predominately influenced by the South Atlantic anticyclone (SAA; Santos and Silva 2013; Pes et al. 2017; Gilliland and Keim 2018). Future climate forecasts show that the position of the SAA is anticipated to shift to the south and west into the twenty-first century based on different global warming scenarios (Degola 2013). It is critical to consider how spatial changes in the location of the SAA could impact surface conditions across Brazil. A historical examination of the SAA shows that the semipermanent system has been well studied over the last 30 years. Much of the research explains the spatial and synoptic characteristics of the high pressure system in the South Atlantic basin (SAB; Hastenrath 1985; Mächel et al. 1998; Ito and Ambrizzi 2000; Degola 2013; Sun et al. 2017). Hastenrath (1985) first analyzed the interannual behavior and cycle of the SAA and found that the anticyclone reaches its most northward latitude

DOI: 10.1175/JAMC-D-17-0178.1 Ó 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

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in austral summer (DJF) and westward longitude during southern winter (JJA) based on five years of data. This initial study was expanded upon by Mächel et al. (1998), who documented circulation center trends based on position (latitude and longitude) and sea level pressure (SLP) gradient and trends in the SLP core using three temporal periods (1881–1989, 1950–89, and 1970–89) for summer [December–March (DJFM)] and fall [June–September (JJAS)]. The study described a relationship between the latitudinal position of the intertropical convergence zone (ITCZ) and SAA during 1950–89 and 1970–89. When both systems move northward, it results in an increase of SLP gradient, whereas a southward shift causes a decrease in SLP over the SAB during austral summer. This decrease in SLP over the SAB can be connected to the central pressure of the SAA. Sun et al. (2017) showed that the strength of the SAA was at its weakest when the anticyclone was displaced equatorward and eastward during southern summer. The study also identified that the intensity and size of the SAA follows an interannual cycle, where the largest extent and strength of the anticyclone developed during the peak of austral winter (July). With the location, Degola (2013) similarly examined the characteristics of the SAA to found that surface winds were contingent upon the longitudinal position of the SAA for northeastern Brazil between 1989 and 2010. As a result of the SAA shifts, other oceanic and atmospheric mechanisms respond, which may influence the interannual variability and annual cycle of rainfall in the tropical Atlantic Ocean and northeast Brazil (Hastenrath 1976; Hastenrath and Heller 1977; Hastenrath 1984; Hastenrath and Greischar 1993; Kapala et al. 1998). Another area of interest has focused on examining the position of the SAA through atmospheric teleconnections. Sun et al. (2017) analyzed the characteristics of the SAA based on 850-hPa geopotential height data available from atmospheric reanalysis products during 1979–2015. This study compared the austral summer mean location of the SAA with the southern annular mode (SAM; Marshall 2003) and extended multivariate El Niño–Southern Oscillation (ENSO) index (MEI; Wolter and Timlin 2011). Sun et al. (2017) found that when the SAA shifts poleward, the SAM was in a positive phase of La Niña. However, this relationship with the SAA is reversed (equatorward) when the SAM was in a negative phase during an El Niño. This shift in the SAA can also be described with the wind speeds observed across Brazil. Santos and Silva (2013) identified that surface winds in the states of northeastern Brazil revealed contrasting anomalies during the different phases of ENSO for five station groupings between 1986 and 2011. During an El Niño phase, positive wind anomalies were observed, whereas negative wind anomalies developed during a La Niña phase. However,

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Lübbecke et al. (2014) suggested that strength of the SAA and wind power anomalies over the tropical Atlantic was not symmetrical with the timing of the seasonal SST cold tongue and ENSO events for the period 1980–2008. During cold (La Niña) events, a strong SAA in February and March caused an early onset of the cold tongue to develop, which led to above-normal wind anomalies from February to June. This pattern is switched for warm (El Niño) events when the SAA is weak through the month of May, which delayed the arrival of the cold tongue and caused warmer SSTs and negative wind power anomalies to occur in the tropical Atlantic (Lübbecke et al. 2014). Much of the recent wind research conducted on Brazil has emphasized on evaluating future wind energy production (Pereira de Lucena et al. 2010; Pereira et al. 2013; Reboita et al. 2018). Pereira de Lucena et al. (2010) showed that near-surface winds (10 m) for coastal and northern Brazil will increase by the end of the twenty-first century (2070–2100) based on pessimistic high (A2) and optimistic low (B2) scenarios from the Intergovernmental Panel on Climate Change (IPCC). Pereira et al. (2013) simulated wind power density based on IPCC A1B scenario and found that power density is expected to increase across northeastern Brazil. Reboita et al. (2018) examined power density (100 m) for the continent of South America based on representative concentration pathway (RCP8.5) scenario provided by the IPCC for the near (2020–50) and far (2070–98) future. The study concluded that wind density is expected to increase across northern and central-east Brazil in the far future. Reboita et al. (2018) also anticipated the power density forecasted for the eastern part of northeastern Brazil will be adequate for continued wind energy production. These wind forecasts are critical to the states of northeastern Brazil, which specifically account for 79% (370) and 98% (145) of the national operational and plan sites in Brazil (ANEEL 2017). Any changes in the climatological position of the SAA could influence wind speeds across Brazil, which would affect wind energy production, especially for northeastern Brazil. As such, the goal of this research is to construct a spatial and geographic climatology that examines the daily relationship between Brazilian surface wind characteristics and the position of the SAA within the SAB from 1980 to 2014.

2. Data and methods a. Reanalysis datasets Three reanalysis datasets were selected to analyze the surface wind characteristics associated with the central location of the SAA in the SAB. The period of 1980– 2014 was chosen based on the temporal availability of

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each climate reanalysis used in the study. The National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis dataset (Reanalysis-1) is a 2.58 3 2.58 (T62) global resolution model that assimilates meteorological components for 28 vertical levels of the atmosphere from 1948 to present (Kalnay et al. 1996). The NCEP–Department of Energy (DOE) reanalysis dataset (Reanalysis-2) is an improved form of Reanalysis-1, which includes an enhanced spatial resolution (1.8758 3 1.8758) of surface and atmospheric conditions from 1979 to present (Kanamitsu et al. 2002). The European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim) is a 0.758 3 0.758 (T255) global atmospheric assimilated model constructed at 60 vertical levels for the period of 1979 to present (Dee et al. 2011). Data obtained from each reanalysis product consist of 6-hourly (0000, 0600, 1200, and 1800 UTC) 10-m u (west–east) and y (south–north) wind components, SLP, and 2-m air temperature, which was used to calculated daily, seasonal, and annual mean grid point values for Brazil and the SAB during 1980–2014. Resultant surface wind speeds for each grid point are determined based on the u and y wind components obtained from Reanalysis1, Reanalysis-2, and ERA-Interim.

b. Identify SAA center in the SAB Overall daily mean SLPs (based on the 4-times-daily observations) were calculated for each reanalysis grid point found within the SAB during the period of 1980 to 2014. The preliminary domain of 108–508S and 608W–208E was used to identify the daily center of high pressure in the SAB, which follows similar boundaries employed by earlier analyses (i.e., Ito and Ambrizzi 2000; Castro et al. 2015). Degola (2013) used monthly mean SLPs to determine the central location of pressure for the SAA instead of daily mean SLPs because of the concern that migrating anticyclones and fragmented pressure centers in the SAB may disrupt the analysis when approximating the true mean position of the semipermanent SAA. Ito and Ambrizzi (2000) also found that the SAA shifts from its climatological position approximately every 4–5 days because of midlatitude dynamics and frontal passages that occur in the SAB during austral winter. While these issues are acknowledged, this study still asserts identifying the daily position of the maximum pressure center in the SAB is the most appropriate method to provide a comprehensive background on the surface wind characteristics of Brazil. Upon selecting a defined study area for conducting a daily analysis, an algorithm was developed to determine the daily mean latitude and longitude location

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FIG. 1. An example of the algorithm employed to identify the daily mean center of the SAB high pressure system (label F) for 1 Jan 1980 from Reanalysis-1. The initial black box (label A) represents the boundary used to select grid values that follow the criterion (labels B–E) set by the algorithm for each reanalysis used in the study.

of the maximum pressure center within the SAB for Reanalysis-1, Reanalysis-2, and ERA-Interim from 1980 to 2014. Previous studies have implemented second-order Taylor series (Murray and Simmonds 1991), nearest-neighbor (Sinclair 1997; Blender and Schubert 2000; Ito and Ambrizzi 2000; Wernli and Schwierz 2006; Zarrin et al. 2010; Degola 2013), and threshold-based (Davis et al. 1997) algorithms to identify the latitudinal and longitudinal center of pressure. For this study, the daily center of the SAA is derived using a simple mean-based algorithm (Fig. 1). The initial step of the algorithm first calculates the mean SLP based on the preliminary boundary of 108–508S and 608W–208E (label A), which selects SLP grid points that are greater than the daily average for further processing (label B). This sequence is repeated for two additional computations using the remaining grid SLP values selected from the previous step to find the location center of the SAA (labels C and D). As the final task of the algorithm, all remaining SLPs that are one standard deviation above the mean (label E) are used to calculate the daily mean latitude and longitude center of the SAA for the SAB (label F). It should be noted that passing anticyclones that typically form during austral winter and spring in the Southern Hemisphere are subject to be included in the dataset if the algorithm determines it to be the maximum pressure center for that given day. Pezza and Ambrizzi (2005) found that when transient anticyclones eventually dissipate over the SAB, they tend to feed or reinforce the semipermanent feature in the SAB. Therefore, any migrating anticyclones that enter into the study domain can be identified and referred to

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as the maximum pressure system (SAA) as part of this analysis.

c. Surface wind speed and SAA characteristics of the SAB Nonparametric statistics are used to describe the linear trends and relationships that may exist between the latitudinal and longitudinal position of the SAA and Brazilian wind speed characteristics for each reanalysis dataset. Sen’s slope estimator (Sen 1968) is utilized to evaluate seasonal and annual mean wind speed, SLP, and positional trends based on spatial and temporal patterns for the SAB. Next, daily surface characteristics (wind speed, SLP, and air temperature) are correlated with the mean location of central pressure for the SAB using the Mann–Kendall test (Mann 1945; Kendall 1975) to determine if any geographic patterns exist across Brazil. It is important to document the geographical coordinate system used as part of this statistical analysis follows the protocol that a negative latitude is below the equator, while a negative longitude is west of the prime meridian. To further quantify the geographic correlations, wind speed, SLP, and air temperature anomalies were calculated and compared with the central location of the SAA from 1980 to 2014. Individual surface anomalies were constructed based on the annual mean of each grid point. Positive and negative anomaly box plots identified how the annual mean latitude and longitude position of the SAA varies between the five regions of Brazil. Figure 2 shows the geographical regions used to assigned surface anomalies into one of five regions based on Instituto Brasilerio de Geografia e Estatística (IBGE). Based on these results, an average surface wind anomaly for each grid point is calculated on the position of the SAA for five different near-surface wind patterns. The purpose of the selected patterns is to relate the position of the SAA to wind speed across Brazil during the 35-yr study period.

3. Results a. Linear trends of surface wind speed and SAA for the SAB Figure 3 demonstrates seasonal and annual mean linear wind speed trends in the SAB and adjacent South America during 1980–2014. Positive wind trends found in the study are primarily located in two geographic areas during summer (DJF) and autumn (MAM) (Figs. 3a,b). First, increasing surface winds are found poleward of 208S over the SAB for Reanalysis-1 and Reanalysis-2. The second wind axis is located along the equatorial coast of Brazil in Reanalysis-1, which extends

FIG. 2. The five geographical regions (i.e., north, northeast, central-west, south, and southeast) of Brazil defined by the IBGE used to interpret the surface wind characteristics associated with the SAA pressure center in the SAB.

across the SAB toward Africa in Reanalysis-2 and ERA-Interim. The linear wind trends detected are related to SLP and sea surface temperature (SST) changes occurring within the SAB. Vizy and Cook (2016) found that SST is linearly decreasing and SLP is increasing over the subtropical SAB (188–258S and 308W–08) based on atmospheric and oceanic reanalysis products during the dry seasons from 1982 to 2013. However, when located outside the SAA, positive SST correlations are exhibited in the equatorial waters of the SAB (Bombardi and Carvalho 2011). This spatial relationship between the SST boundaries results in a positive pressure gradient to form, which causes surface winds to increase equatorward and poleward of the SAA. Consequently, this type of SST pattern is consistent to that of the South Atlantic dipole (Venegas et al. 1997; Sterl and Hazeleger 2003; Bombardi and Carvalho 2011; Nnamchi et al. 2011). During austral winter (JJA) and spring (SON), positive linear wind trends found are related to oceanic surface wind patterns found over the SAB (Figs. 3c,d). The strongest positive trends were found along the South Equatorial Current (SEC), where wind speeds have increased more than 0.3 m s21 decade21 for Reanalysis-1 and Reanalysis-2 and 0.1 m s21 decade21 for ERA-Interim. This spatial linear trend occurs because of the northward shift of the ITCZ and SAA, which relocates the orientation of SST gradients between 58S and 158S and results in transporting cooler SSTs from the Benguela Current into the equatorial waters (Grodsky and Carton 2003). A temporal analysis also shows that SSTs found along the SEC have cooled based on Advanced Very High Resolution Radiometer

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FIG. 3. (a) DJF, (b) MAM, (c) JJA, (d) SON, and (e) annual spatial distribution of surface wind speed trends (m s21 decade21) with average wind directions for the SAB based upon Reanalysis-1, Reanalysis-2, and ERA-Interim from 1980 to 2014. Surface ocean currents SEC and South Equatorial Countercurrent (SECC) for the SAB are based on Cabos et al. (2017).

(AVHRR) Pathfinder version 5 data from January 1985 to December 2004 (Good et al. 2007). However, Vizy and Cook (2016) found that SSTs have increased between 188 and 258S during austral winter and spring for

the period of 1982–2013. This contrasting linear SST trend found between the cooling equatorial and warming subtropical waters allows a larger thermal gradient to develop on the northward side of the SAA. With this

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temperature gradient change, southeastern trade winds along the SEC would increase as described by each reanalysis during austral winter and spring. While each reanalysis shows increasing seasonal trends across the SAB, negative wind trends are still documented over the continent of South America. The primary axis of negative trends described is found across southeastern and southern Brazil during austral summer and autumn (Figs. 3a,b). Current research supports the decreasing surface wind trend pattern found within the interior of southern and southeastern Brazil (Pes et al. 2017; Gilliland and Keim 2018). A possible reason for the decline in wind speed trends may be correlated to changes observed in surface air temperature. Klink (1999) explained that wind trends found across the United States were related to modifications in pressure and temperature gradients that develop at higher latitudes. Similarly, previous studies showed that minimum surface air temperatures have increased over southern Brazil (Marengo and Camargo 2008; Sansigolo and Kayano 2010). This change in minimum surface air temperature has been correlated with a decline in the diurnal temperature range for southern Brazil (Vincent et al. 2005). It is expected that changes in surface winds resulting from atmospheric warming could also be connected to climatological shifts in midlatitude dynamics. A temporal analysis by Archer and Caldeira (2008) showed that the position of the subtropical jet (STJ) has moved poleward between 1979 and 2001 based on Reanalysis-1 and ERA-Interim datasets. With this shift of the STJ, studies have documented that the number of extratropical cyclones passing through the midlatitudes have decreased and migrated southward (Pezza and Ambrizzi 2003; Wang et al. 2006; Solman and Orlanski 2014). Consequently, this latitudinal shift of midlatitude systems has an effect on the zonal and meridional wind and temperature gradients found across South America. Recent work has showed that tropospheric zonal winds located between 108 and 208S have decreased, while meridional winds have increased during the period 1979–2005 (Allen and Sherwood 2008). This alteration of the tropospheric winds results in the meridional temperature gradient to increase in the subtropical and middle latitudes of Brazil. With this tropospheric warming, the pressure gradient decreases and the surface winds across southern and southeastern Brazil decline. The geographic analysis of surface wind speed trends performed from this study demonstrates that changes in macroscale atmospheric circulation are a possible reason for the change in surface winds being observed across Brazil and the SAB. It is important to examine the geographical and temporal characteristics of the

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SAA in the SAB. Figure 4 shows the mean seasonal and annual position of the SAA by year for each reanalysis dataset during the 35-yr study period. The average location of the SAA follows a cyclical pattern, where the anticyclone is at its most poleward position during austral summer and autumn and closest to the equator in winter and spring. While this geographical pattern is consistent of previous studies, the mean location of the SAA is positioned south and west of its normal climatological position in the SAB (Hastenrath 1985; Mächel et al. 1998; Degola 2013; Sun et al. 2017). Overall, the spatial variability of the SAA is defined to the domain of 308–408S and 208–108W (Fig. 4). Seasonal scatterplots show that the SAA center is confined between 28 and 38 latitude and 38 and 98 longitude of its climatological mean for each reanalysis. In austral autumn and winter, the longitudinal location of the SAA is at its highest variability. Degola (2013) similarly identified that the largest longitudinal shifts occurred from March to September for the SAA. This is also consistent with Sun et al. (2017) who found that synoptic variability is at its peak during the months of May to September in the Southern Hemisphere. After this period, the longitudinal spread starts to dissipate in the final months of spring (October and November), with the smallest zonal distribution occurring in austral summer. Figure 5 illustrates the seasonal and annual trends based on latitudinal and longitudinal mean center of the SAA between 1980 and 2014. It is evident that two distinct patterns are documented based on latitude and longitude positions in the SAB. Reanalysis datasets show a poleward shift in the latitudinal center of the SAA for each seasonal and annual interval, with the largest relocations developing during autumn and the smallest variations occurring in winter. This southward shift of the SAA is supported by a linear increase of SLP occurring at higher latitudes across the SAB (Vizy and Cook 2016). With this poleward shift in SLP, Degola (2013) showed a continued southward shift of the SAA into the early portion of the twenty-first century based on six future warming scenarios. However, a higher degree of seasonal variability exists when analyzing longitudinal trend position of the SAA center for the SAB (Figs. 4 and 5). Results show that the longitudinal center for austral winter and spring are trending in opposite directions, while summer and autumn portray marginal changes in longitude. Degola (2013) suggests that the interannual longitude variability during winter and spring is influenced by extratropical cyclone activity in the SAB. Similarly, Sun et al. (2017) found that extratropical cyclone activity is a physical mechanism that influences the zonal position of the SAA for austral winter between 1979 and 2015 based on

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FIG. 4. Average position (colored circles) of the SAA by year for (a) DJF, (b) MAM, (c) JJA, (d) SON, and (e) annual from Reanalysis-1 (red), and Reanalysis-2 (blue), and ERA-Interim (green) between 1980 and 2014. An arrow shows the expected path of the SAA based on the latitudinal and longitudinal trends calculated from Sen’s slope estimator.

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TABLE 1. Statistical regression (hPa decade21) analysis of overall seasonal and annual SLP of the SAA based on reanalysis datasets between 1980 and 2014. A linear trend highlighted in boldface font indicates that the regression is statistically significant at the 95% confidence level.

FIG. 5. Seasonal and annual latitudinal and longitudinal trends of the SAA based on Reanalysis-1 (red), Reanalysis-2 (blue), and ERA-Interim (green) during 1980–2014.

ERA-Interim. Ito (1999) further documented that the SAA tends to be located east of its climatological mean when frontal passages are frequent and shifted to the west when there is less frequent to nonexistent extratropical activity in the SAB. It is also plausible that the frequency or intensity of extratropical cyclones in the Southern Hemisphere has changed over time, which has allowed the longitudinal trends observed during spring and winter to be identified in this analysis. Reboita et al. (2015) determined the number of Southern Hemisphere extratropical cyclone occurrences has increased during austral winter, while remaining relatively constant through spring for the period 1980–2012. The study also documented a positive temporal trend in cyclonic activity over southern Brazil during winter. Therefore, this increased frequency of extratropical cyclones moving over southern Brazil would support the eastward shift of the SAA found with Reanalysis-1, Reanalysis-2, and ERA-Interim. Although the climatological position is important to investigate, it is also necessary to examine how the intensity of the SAA has evolved. Table 1 shows that the central pressure of the SAA has changed for each of the three reanalysis datasets between 1980 and 2014. Each reanalysis shows that the SAA has strengthen during all seasonal and annual periods, with the highest positive SLP changes occurring during the transition seasons (autumn and spring). These findings are consistent with Vizy and Cook (2016) who found positive SLP trends over the subtropical latitudes of the SAB between 1982 and 2013. Sun et al. (2017) also identified that the maximum intensity of the SAA occurred when the anticyclone was latitudinally positioned poleward in the SAB. Consequently, the strength of the SLP trends found for each season follows the latitudinal shifts found

Dataset

DJF

MAM

JJA

SON

Annual

Reanalysis-1 Reanalysis-2 ERA-Interim

0.42 0.50 0.38

0.67 0.74 0.52

0.39 0.48 0.18

0.52 0.67 0.40

0.50 0.57 0.33

in Fig. 5. This connection between SLP and location suggest that other atmospheric systems are influencing the temporal trends found in this analysis. Recent work identified that the poleward shift in SLP could be related to the thermal widening of the tropical belt (Hadley cell) developing at the equator (Fu et al. 2006; Lu et al. 2007; Seidel and Randel 2007; Seidel et al. 2008; Choi et al. 2014; Lucas et al. 2014). With this atmospheric warming, climate change predictions forecast that the position of the SAA will continue to shift poleward and intensify into the twenty-first century based on different emission scenarios (Nuñez et al. 2009; Soares and Marengo 2009; Marengo et al. 2010; Seth et al. 2010; Degola 2013). Though the linear trends of position and intensity appear similar, differences still exist among the reanalyses. An examination of ERA-Interim shows a lesser degree of consensus in the strength and position of the SAA relative to Reanalysis-2. ERA-Interim describes a small intensification of the central pressure, while Reanalysis-2 exhibits a more poleward and westward shift of the strengthening anticyclone during the 35-yr study period. The largest spatial disagreement found between both reanalyses occurs during austral winter and spring. Vizy and Cook (2016) showed that rate of cooling and warming found over the subtropical South Atlantic waters was higher in Reanalysis-2 than ERA-Interim. This variation in SST along with other ocean and atmospheric mechanisms can influence the intensity and position of the SAA in the SAB. Wind trends located between the South Atlantic Current and SEC illustrate this disparity found among the datasets (Fig. 3). ERA-Interim depicts decreasing wind speeds, whereas Reanalysis-2 shows positive wind trends during each season. These differences in SLP, wind, and location results from the assimilation procedures used to forecast atmospheric and oceanic conditions in the SAB.

b. Brazilian surface correlations based on the SAA With the continual geographic and temporal shifting of the SAA center in the SAB, it is important to understand how wind speeds change across Brazil as a

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result of the movement of the high pressure center (Figs. 6 and 7). Figure 6 shows the geographic seasonal and annual correlations between daily mean wind speed and latitudinal pressure center during the 35-yr study period. When the latitudinal center shifts to lower latitudes, wind speeds across northern and northeastern Brazil increase, while a decline in speed is observed in southern and southeastern Brazil. Central-west Brazil and the eastern Amazon act as a transitional boundary between northern and southern Brazil, which causes a lower degree of correlation to exist for each season. A spatial pattern is also observed when analyzing daily mean wind speeds with longitudinal position of the SAA for the SAB (Fig. 7). It shows that when the center of the SAA shifts to the east, wind speeds decrease across Brazil. However, when the anticyclone is located to the west, positive wind correlations are observed throughout the study area. These results support the findings of Degola (2013) who concluded that surface u winds across eastern Brazil were stronger when the SAA is located west of its climatological mean position in the SAB. The study also found that weaker zonal winds developed when the SAA is situated east of its normal position in the SAB. The only region where the longitudinal position does not correlate with wind speed is in the western Amazon during southern summer (Fig. 7a). This is a result of the equatorial trough (i.e., ITCZ) that develops over the Amazon basin, which controls the daily weather conditions observed during southern summer (Hastenrath 1985). To understand how these changes in wind speeds are linked to other atmospheric conditions as they are in turn associated with the SAA, daily mean SLP and surface air temperature were examined with respect to latitude and longitude at a regional scale. Each grid point located over Brazil was assigned to one of five regions established by IBGE. After each point was assigned to a region, individual correlations were performed to determine how SLP and temperature are geographically related to the SAA within the SAB. Figure 8 shows the annual SLP and surface temperature correlations based on the central latitude and longitude position of the SAA for the country of Brazil during 1980–2014. As expected, when the anticyclone moves north and west, SLP increases and air temperature decreases across northern and northeastern Brazil (Figs. 8a,b). This pattern is reversed for southern Brazil, where SLP decreases and air temperature increases when the SAA is located south and east of its average position. These spatial correlations found across Brazil are supported by the presence of moisture and clouds provided by the SAA in the SAB. De Lima Moscati and Gan (2007) found that when the SAA is located south

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and west of its normal position, it allowed the southeastern trade winds to strengthen and transports moisture into northeastern Brazil, which enabled cloud formation and precipitation to develop and a decrease in temperature to occur. As for southern and southeastern Brazil, temperature changes develop when oceanic moisture transported by the northeastern trade winds associated with the SAA and heat from the interior of Brazil converge to form convective thunderstorms (Reboita et al. 2010). Degola (2013) found that surface temperature anomalies for southern Brazil increase when the SAA is shifted west of its climatological mean for southern Brazil. However, this analysis showed that temperatures increased when the zonal position of the SAA is shifted away from the South American continent. As a result, it is important to further evaluate the role of latitude and longitude on surface winds, SLP, and temperature across the five different regions of Brazil.

c. SAA characteristics based on surface anomalies Figure 9 displays the regional mean latitude and longitude positions of wind speed, SLP, and temperature anomalies for each reanalysis. Above-normal wind speeds for northern, northeastern, and central-west Brazil occur when the SAA center is located at lower latitudes (Fig. 9a; left panel). A contrasting wind pattern is described for southeastern and southern Brazil, where lower wind speeds occur when the anticyclone is situated poleward of its average position. This change in surface wind speeds results because of alterations in SLP and temperature when the SAA shifts within the SAB. As the SAA shifts equatorward, it causes SLP to rise across northern, northeastern, central-west, and southeastern Brazil (Fig. 9b, left panel) and temperature to fall across all regions (Fig. 9c, left panel). These surface conditions develop when the u surface winds flow across the SAB, which transports oceanic air into coastal Brazil. The resulting thermal and pressure differences established between the land and ocean cause the gradients to increase, which in response allows wind speeds to increase across the mountains and plateaus of northern and northeastern Brazil. Santos and Silva (2013) found a similar wind pattern in northeastern Brazil for which they attribute sea breeze, SAA, and topography as the reasons why stronger wind speeds were observed across the study area. The opposite pattern is described for southern Brazil, where SLP decreases while the SAA center is located at lower latitudes. A longitudinal wind pattern is also described for Brazil (Fig. 9a, right panel). When the SAA is located closer to South America, wind speeds increase and surface temperatures decrease across all regions of Brazil. A reversed temperature (increased) and wind

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FIG. 6. Spatial correlations between wind speed and latitudinal center of the SAA for (a) DJF, (b) MAM, (c) JJA, (d) SON, and (e) annual for Reanalysis-1, Reanalysis-2, and ERA-Interim during 1980–2014. Hatched areas indicate that the correlation is statistically significant at the 95% confidence level.

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FIG. 7. As in Fig. 6, but for longitude.

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FIG. 8. Annual regional latitudinal and longitudinal correlations between the daily mean position of the SAA and (a) SLP and (b) temperature for Reanalysis-1 (circles), Reanalysis-2 (squares), and ERA-Interim (triangles).

(decrease) pattern is described when the anticyclone is farther from the continent. By contrast, Degola (2013) found that surface temperatures decreased and increased when the SAA was located east and west of its climatological mean position for southeastern and southern Brazil based on two average synoptic conditions of September 1993 and 2007. This conflicting result with Degola (2013) is related to the usage of monthly instead of daily averages, which cannot distinguish migratory anticyclones in the higher latitudes of the SAB. The zonal tracking of these transient high pressure systems typically bring below-normal temperatures and increased barometric pressure values to southern Brazil (Satyamurty et al. 1998), which is supported by the ridgeto-trough (i.e., southwest-to-northeast in the Southern Hemisphere) propagation of Rossby waves that originate from the tropical western Pacific (Marengo et al. 2002). This relationship between air temperature and SLP causes the gradients to increase, which results in the increase

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of wind speeds across the region. Once the migratory anticyclone shifts away from the continent, temperatures increase while SLP and wind speeds fall for the northern latitudes of Brazil. This life cycle follows the results of Sinclair (1996), which documented the initial formation of the passing anticyclone over the continent of South America, intensification off the coast of South America, and eventual dissipation on the eastern side of the SAB. A low spatial variability is observed in SLP anomalies for northern and northeastern Brazil, whereas these pressure anomalies become more variable for centralwest, southeastern, and southern Brazil (Fig. 9b; right panel). Similarly, the variability of temperature is greatly dependent on the position of the SAA for southeastern and southern Brazil (Fig. 9c, right panel). The deviation between SLP and temperature anomalies for northern and southern Brazil is influenced by separate two meteorological environments. Atmospheric pressure within equatorial Brazil and Amazon basin changes minimally between austral summer and spring because of the influence of the ITCZ (Schwerdtfeger 1976). Any changes that develop in SLP occur when the ITCZ is seasonally displaced northward, which allows the SAA to migrate equatorward and therefore play a more prominent role of influencing SLP characteristics for the region. On the other hand, the zonal transient anticyclones across the SAB contribute to the annual temperature patterns found, especially for southeastern and southern Brazil. This study has documented that the position of the SAA in the SAB affects surface wind characteristics observed across Brazil. Five surface maps will illustrate how near-surface wind speeds vary across Brazil when the location of the SAA center is situated between a specific range of latitudes and longitudes within the SAB (Fig. 10). First, when the latitudinal center is shifted north of its climatological position, the average wind is above normal for portions of northeastern Brazil and below normal for coastal southern Brazil (Fig. 10a). SLP isobars are oriented parallel to the coast of Brazil, which allows a larger pressure gradient to form, which enables stronger-than-normal zonal winds to be observed across northeastern Brazil. This positive spatial orientation of the SLP found across the subtropical and midlatitudes is supported by Degola (2013). Additionally, this described geographical pattern is consistent with the maximum intensity and size of the SAA, which is found at its most northern and western location during austral winter (Sun et al. 2017). When the SAA center is found off the coast of Uruguay, where onshore flow is present, a change in pressure gradient is shown that causes wind speeds across

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FIG. 9. Regional (left) latitudinal and (right) longitudinal mean position box plots of the SAA based on (a) wind speed, (b) SLP, and (c) air temperature negative (gray) and positive (black) anomalies for Reanalysis-1 (red), Reanalysis-2 (blue), and ERA-Interim (green) during 1980–2014.

interior and southern Brazil to be faster than normal (Fig. 10b). This wind anomaly pattern develops as a result of transient anticyclones that likely form on the lee side of the Andes Mountains and track across the southern portion of the SAB. The formation of the migrating system begins over the continent from lowlevel cooling and as the anticyclone travels over the eastern coast of South America, it undergoes rapid intensification because of intense baroclinic activity in the region (Sinclair 1996). Previous research has documented this anticyclone genesis through the ‘‘budding’’ mechanism, whereby the parent Pacific Ocean anticyclone cell extends a ridge downstream of the Andes Mountains that intensifies and closes off to form a new eastward high pressure cell (cradle) over South America (Taljaard 1967, 1972; Jones and Simmonds 1994). The

downstream formation of this anticyclone advects colder air northward along the Andes Mountains, resulting in below-normal temperatures (i.e., cold surges) to occur in southern and southeastern Brazil (Garreaud 2000; Lupo et al. 2001; Pezza and Ambrizzi 2005; Sprenger et al. 2013). While this synoptic setup is occurring, the thermal low located over central South America is displaced equatorward, which allows the SLP gradient to increase and positive wind anomalies to be observed across the interior of Brazil as described by Fig. 10b. As the SAA shifts back to its normal climatological position (i.e., 358S and 158W), surface winds across Brazil follow their typical average with exception to southern Brazil, where a slight decline in wind speeds is observed (Fig. 10c). Note that the mean position used

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FIG. 10. Mean wind speed anomalies (m s21), wind direction, and SLP (hPa) based on the location of the SAA when located between (a) 208 and 308S and 308 and 208W, (b) 308 and 408S and 608 and 508W, (c) 308 and 408S and 208 and 108W, (d) 408 and 508S and 508 and 408W, and (e) 408 and 508S and 108W and 08 for Reanalysis-1, Reanalysis-2, and ERA-Interim.

for this study is located south and west of previous studies (Hastenrath 1985; Mächel et al. 1998; Degola 2013; Sun et al. 2017), which perform monthly or seasonal instead of daily analysis to identify the central

location of the SAA in the SAB. It is plausible that using monthly or seasonal latitude and longitude positions could change the average wind speed anomalies found for Fig. 10c, which warrants additional analysis. Last,

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when the SAA is shifted to the south (Fig. 10d) and east (Fig. 10e), it reveals weaker-than-normal mean wind speeds across eastern Brazil. These negative wind anomalies develop because of the poleward shift of the ITCZ, which decreases SLP gradients across the country. This decrease in wind speed is also noted across the SEC, which influences surface winds observed across northern and northeastern Brazil. As a result, when the thermal and pressure gradients decline along the SEC, it weakens the coastal land–sea breeze, which causes negative wind anomalies to be reported across eastern Brazil.

4. Conclusions This study examined the surface wind characteristics of Brazil based on the position of the SAA in the SAB using three reanalysis datasets. Temporal linear increases in wind speed across Brazil are related to the seasonal relationship between the ITCZ and SAA, especially for northern and northeastern Brazil. However, temporally decreasing linear trends in wind speed across southeastern Brazil may be related to changes in midlatitude dynamics observed during the last century. As a result of this relationship with macroscale atmospheric circulations, this study identified the daily position of the SAA center to determine if any seasonal or annual temporal trends exist during 1980–2014. It was documented that climatological mean position of the SAA has shifted farther east during southern winter and to the west in the spring. Previous work has shown that passing midlatitude cyclones affect the location of the SAA in the SAB (Ito 1999; Degola 2013; Sun et al. 2017). Results indicate the frequency or location of extratropical cyclones passing through the SAB could be causing the opposing longitudinal trends found in austral winter and spring (Reboita et al. 2015). Future research should investigate zonal wind modifications at shorter or more specific time scales in order to determine which other climatic (i.e., atmospheric teleconnections) factors could be contributing to the changes being observed with the position of the SAA. The Mann–Kendall test (Mann 1945; Kendall 1975) was used to show how the location of the SAA correlates with wind speed, SLP, and air temperature across Brazil. When the SAA center is located at lower latitudes, SLP increases northward while temperature decreases westward, which in response allows wind speeds to increase across northern and northeastern Brazil during austral summer. This surface pattern develops when the ITCZ is displaced northward, allowing higher SLPs and cooler surface conditions to be transported in from the SAB. As the ITCZ eventually shifts toward the equator, longitudinal correlations with wind speed, SLP, and temperature indicate how the position of central pressure

affects conditions across Brazil differently. These results demonstrated that SLP correlations across Brazil are associated with changes in latitude, while temperature is connected to longitudinal shifts in the anticyclone. To further quantify these results, this study analyzed the relationship of positive and negative wind, SLP, and temperature anomalies based on the location of the SAA for the SAB. For the northern half of Brazil, wind speed anomalies tend to be related to changes in SLP, while southern Brazil is more strongly connected to alterations in temperature. The role of migrating anticyclones has been identified as a possible reason of influencing surface wind characteristics across southern and southeastern Brazil. These findings are further described when analyzing the average wind speed anomalies based on the location of the SAA center in different geographic locations across Brazil. Strongerthan-normal wind speeds are typically observed when the location of the SAA center is located north and west of its mean position in the SAB. This spatial wind pattern is reversed when the SAA positioned south and east of its normal location, where wind anomalies across Brazil are below normal. The location and position of the SAA in the SAB plays an important role in influencing wind characteristics across Brazil. Any latitude or longitude change in the SAA can affect the daily weather conditions observed across Brazil. The consistent wind flow across portions of northeastern Brazil has been identified as a potential area to develop and generate renewable wind energy (Camargo do Amarante et al. 2001). As a result, any climatological shifting of the SAA could affect wind energy production across the region. Future wind forecasts expect that wind speeds will continue to increase by the end of the twenty-first century for northeastern Brazil (Pereira de Lucena et al. 2010; Pereira et al. 2013; Reboita et al. 2018). These wind forecasts are based on the scenario that the longitudinal position of the SAA will shift toward the west over the next century (Degola 2013). Consequently, it is essential to continue to evaluate and understand how atmospheric systems evolve across the SAB, so that future climatological and socioeconomic consequences of wind on Brazil can be identified. Acknowledgments. The authors thank the three anonymous reviewers for their helpful suggestions and comments. REFERENCES Abhishek, A., J.-Y. Lee, T. C. Keener, and Y. J. Yang, 2010: Longterm wind speed variations for three Midwestern U.S. cities. J. Air Waste Manage. Assoc., 60, 1057–1064, https://doi.org/ 10.3155/1047-3289.60.9.1057.

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